Duckworth, P orcid.org/0000-0001-9052-6919, Alomari, M orcid.org/0000-0002-6565-4887, Gatsoulis, Y et al. (2 more authors) (2016) Unsupervised Activity Recognition using Latent Semantic Analysis on a Mobile Robot. In: Kaminka, GA, Fox, M, Bouquet, P, Hüllermeier, E, Dignum, V, Dignum, F and Van Harmelen, F, (eds.) Proceedings. ECAI 2016: 22nd European Conference on Artificial Intelligence, 29 Aug - 02 Sep 2016, The Hague, Netherlands. Frontiers in Artificial Intelligence and Applications (285). IOS Press , Amsterdam, Netherlands , pp. 1062-1070. ISBN 978-1-61499-671-2
Abstract
We show that by using qualitative spatio-temporal abstraction methods, we can learn common human movements and activities from long term observation by a mobile robot. Our novel framework encodes multiple qualitative abstractions of RGBD video from detected activities performed by a human as encoded by a skeleton pose estimator. Analogously to informational retrieval in text corpora, we use Latent Semantic Analysis (LSA) to uncover latent, semantically meaningful, concepts in an unsupervised manner, where the vocabulary is occurrences of qualitative spatio-temporal features extracted from video clips, and the discovered concepts are regarded as activity classes. The limited field of view of a mobile robot represents a particular challenge, owing to the obscured, partial and noisy human detections and skeleton pose-estimates from its environment. We show that the abstraction into a qualitative space helps the robot to generalise and compare multiple noisy and partial observations in a real world dataset and that a vocabulary of latent activity classes (expressed using qualitative features) can be recovered.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 The Authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EU - European Union FP7-ICT-600623 |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Jul 2016 11:42 |
Last Modified: | 13 Dec 2024 15:37 |
Published Version: | https://doi.org/10.3233/978-1-61499-672-9-1062 |
Status: | Published |
Publisher: | IOS Press |
Series Name: | Frontiers in Artificial Intelligence and Applications |
Identification Number: | 10.3233/978-1-61499-672-9-1062 |
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Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:103049 |